The Complete Guide to Using AI in the Financial Services Industry in Singapore in 2025

Singapore's financial services sector in 2025 is racing to turn national ambition into real workflows: IMDA's push to build an “AI‑fluent” workforce and pulse data showing three in four workers now using AI reflect a people‑first roll‑out, while banks and fintechs scale models and measurable value - DBS alone runs hundreds of models and OCBC and others report widespread AI decisions - so institutions are balancing productivity gains with governance and training needs.

With the National AI Strategy 2.0 and heavy public‑private investment underpinning the market (projected to reach multi‑billion USD scale), firms are prioritising upskilling, model risk management and practical tools; teams can start by learning applied promptcraft and workplace AI skills through courses like the AI Essentials for Work bootcamp (Nucamp), while keeping an eye on IMDA's workforce programmes and Singapore's infrastructure push described in the IMDA press release on building an AI‑fluent workforce and reporting in analysis of Singapore's $27B AI investment (Introl).

Bootcamp	Key details
AI Essentials for Work	15 weeks; practical AI skills, prompt writing, job‑based AI use; early bird $3,582; syllabus: AI Essentials for Work syllabus; register: Register for AI Essentials for Work

"To support this strategy and further catalyse AI activities, I will invest more than $1 billion over the next five years into AI compute, talent, and industry development." - Prime Minister Lawrence Wong (Budget 2024)

The near‑term future of finance and accounting AI in Singapore is practical, revenue‑focused and infrastructure‑backed: banks and fintechs are moving from pilots to domain‑specific, governance‑aware deployments that power portfolio optimisation, real‑time risk scoring and hyper‑personalised customer journeys, while RegTech and AML improvements free up human reviewers for higher‑value work; local coverage highlights why this matters - Singapore's compute and investment clout (reporting that the city‑state now generates a startling share of global GPU demand) means firms can train specialised models locally and scale them across ASEAN with low latency and strong oversight.

Expect GenAI to accelerate knowledge‑worker augmentation and copilot tools in accounting and wealth management, tighter explainability and FEAT-style governance, and a business‑first approach where teams pick high‑ROI use cases (chatbots, reconciliation, fraud detection, robo‑advice) before broader transformation.

For concrete trends and use cases see analyses of portfolio optimisation and RegTech opportunities in Singapore's market and the 2025 banking trends that show rapid GenAI deployment across customer service and risk functions.

Metric	Value (source)
Financial institutions with AI solutions	Over 70% (Business+AI)
Banking leaders deploying GenAI (2024)	~75% (Devoteam)
Singapore AI market projection	$1.05B (2024) → $4.64B by 2030 (Introl)

“We believe that by balancing innovation with a strong ethical compass, we can harness the power of AI to enhance our services and benefit our customers and employees.” - Nimish Panchmatia, DBS (Global Finance)

Today in Singapore AI is no longer a lab experiment but a living part of banking workflows: firms deploy machine learning and generative AI across fraud detection and AML, hyper‑personalised customer journeys, automated regulatory reporting, synthetic data for risk testing and employee “copilots” that speed routine work.

Local champions show the scale - DBS runs more than 1,500 models across hundreds of use cases and OCBC reports AI powering some 4 million decisions a day - while UOB has piloted productivity copilots for staff - concrete signs that banks are industrialising AI rather than merely prototyping it.

Providers and RegTechs fill in the stack (from NLP for document review to graph analytics for complex fraud rings), and MAS is seeding industry utilities like NovA!, Veritas and TradeMaster to help firms share safe tooling and talent; MAS's AIDA programme also couples grants and training to accelerate adoption with oversight (see MAS AIDA).

At the same time regulators are pressing for disciplined model risk management - inventorying AI, cross‑functional governance, independent validation and human‑in‑the‑loop controls are now standard expectations - summarised in MAS guidance and expert summaries on model risk management.

The upshot for Singapore financial services: measurable efficiency and new customer experiences, but only where robust controls and clear accountability accompany deployment (for a practical use‑case roundup, see ProCreator's review of generative AI in fintech and MAS's AIDA work).

Singapore's regulatory stance for AI in finance is pragmatic and risk‑based: rather than a single AI law, the Monetary Authority of Singapore has issued targeted guidance - most notably the December 2024 MAS information paper on Artificial Intelligence (AI) Model Risk Management - that lays out good practices around governance, inventories, risk‑materiality assessments, independent validation and lifecycle monitoring; firms are encouraged to treat these as baseline expectations alongside IMDA's voluntary Model AI Governance Framework, AI Verify testing tools and the broader National AI Strategy 2.0, so teams can move fast while keeping clear accountability and human oversight.

MAS's paper signals specific supervisory priorities for banks and insurers (cross‑functional oversight forums, complete AI inventories, proportionate controls and strong third‑party clauses) and warns that generative AI and outsourced models require extra testing, technical guardrails and contingency plans.

For practitioners, the takeaway is simple but vivid: a disciplined AI inventory and materiality framework can turn an operational risk into a manageable control process rather than an unexpected regulatory headache - start by mapping ownership, testing regimes and escalation paths tied to MAS's recommendations and the IMDA toolkits.

Read the MAS information paper for the detailed observations and the legal and industry overviews for broader context.

MAS focus area	Key expectations
Oversight & governance	Cross‑functional forums, clear roles, FEAT/Veritas alignment
Risk systems & processes	Complete AI inventories, risk materiality assessment, monitoring
Development, validation & deployment	Robust testing, independent validation for high‑risk models, change controls

"demonstrates Singapore's strong support in advancing global harmonisation in a practical way."

MAS is turning policy into practical levers that nudge banks and fintechs from experiment to scale: the AIDA programme bundles industry‑wide technical platforms (NovA!, which initially targets sustainability‑linked loans for Singapore's real‑estate sector), ethics and assurance tooling (the Veritas framework that maps AIDA solutions to FEAT principles) and market utilities like TradeMaster for AI quantitative strategies, while funding and talent pipelines close the adoption loop.

Firms can tap co‑funding through the AIDA Grant co-funding details (eligible projects include fraud detection, KYC, regulatory automation and predictive analytics) and work with the MAS AIDA Talent Development Programme details - which links Financial Institutions to Institutes of Higher Learning and training providers - to develop role‑specific curricula and on‑the‑job pathways that address the industry's well‑documented skills gap.

The result is a coordinated stack: open tools and shared platforms, matched grants, and curated training so institutions can deploy powerful, accountable models without reinventing the wheel; see the MAS AIDA programme overview for details and application guidance.

Programme / Project	Key details (source)
MAS AIDA programme overview	Industry platforms: NovA!, Veritas (FEAT), TradeMaster; supports research & R&D
MAS AIDA Talent Development Programme details	Launched 2023; consortium of FIs, IHLs and training providers to build AIDA talent
AIDA Grant co-funding details	Up to S$500,000 cap; ~30% co‑funding for qualifying AIDA projects

Singapore's testing and assurance ecosystem has moved from guidance to practical tooling, with the AI Verify testing framework and open‑source toolkit at its core: organisations can systematically map controls to 11 internationally recognised governance principles (transparency, explainability, robustness, fairness, data governance and more), run technical tests and process checks inside their own environment, and even export results as a Docker container for repeatable assurance and stakeholder reporting - an especially practical approach for GenAI deployments since the framework's May 2025 update.

Launched and stewarded by IMDA and partners, AI Verify's toolkit is designed to complement IMDA's starter kits and Project Moonshot work, and to align with global standards (crosswalks to NIST AI RMF and ISO/IEC 42001 appear in the resources), helping firms demonstrate conformity without leaking sensitive data.

For teams building or buying models, this ecosystem - voluntary, interoperable and community‑driven - turns abstract ethics checklists into executable tests and shareable reports that regulators, auditors and customers can understand; see the AI Verify testing framework and IMDA's launch material for downloads, tutorials and the latest starter kits.

Key element	What it provides
AI Verify testing framework - overview and downloads	11 governance principles; testable criteria and process checks (Traditional & Generative AI)
AI Verify toolkit and resources - technical tests, Docker export and tutorials	Technical tests (SHAP, AIF360, ART), reports, Docker export, tutorials, starter kits for GenAI
IMDA press release: Singapore launches AI Verify Foundation	Open‑source community, industry pilots, international harmonisation and governance convening

"The private sector with their expertise can participate meaningfully to achieve these goals with us." - Minister Josephine Teo (IMDA press release)

Singapore's vendor landscape is a vibrant mix of homegrown fintechs, global AI specialists and RegTechs that together turn policy and infrastructure into live banking features - directories and reports highlight anywhere from hundreds to over a thousand firms operating here, with dedicated clusters in payments, regtech, wealthtech and AML. Payment and embedded‑finance players such as Airwallex and Aspire (the latter servicing 15,000+ SMEs and reporting roughly US$15 billion in annualised payment volume) sit alongside consumer brands like YouTrip (nearly US$10B annualised transaction volume), while platform and AI vendors - from regional names like Nium and Advance Intelligence Group to specialised providers such as Taiger (document NLP), Silent Eight (AML alert triage), Finbots.AI (alternative credit scoring) and DataRobot (model‑building and deployment) - supply the models, copilots and compliance tooling banks need.

For a handy vendor roll call see the Top 15 Fintech Companies in Singapore and a deep dive on AI solution providers that map directly to Singapore use cases and regulatory expectations.

Vendor	Core niche / relevance to Singapore finance
Airwallex	Global payments, treasury and embedded finance platform for cross‑border flows
Aspire	B2B fintech for SMEs - multi‑currency accounts, cards; large payment volumes (~US$15B annualised)
YouTrip	Consumer multi‑currency wallet and debit card; ~US$10B annualised transaction volume
Nium	Global payouts/pay‑ins, card issuance and banking‑as‑a‑service for banks and businesses
Silent Eight	AI for AML alert reduction and explainable triage to cut false positives
DataRobot	Enterprise ML platform to build, test and deploy compliant models without heavy in‑house data science

“I most look forward to seeing the world of FinTech, from companies, investors, technologists, regulators and policymakers, in Singapore, as well as touring the showroom floor, which is like peering into the future of money and banking..." - Dante Disparte

Singapore's most visible real‑world AI story is DBS: Harvard Business School's case and local coverage show a bank that moved from pilots to industrial scale - over 800 AI models across roughly 350 use cases powering hyper‑personalised nudges, proactive SME credit alerts and in‑work GenAI copilots - so the takeaway is concrete, not theoretical.

In practice that looks like a GenAI “CSO Assistant” used by hundreds of customer service officers with near‑100% transcription and solutioning accuracy and up to 20% faster call handling, hyper‑personalised nudges that engaged 8.6 million customers regionally (3 million in Singapore) with large uplifts in saving and investing, and predictive models that flagged >95% of at‑risk SME loans months early to avoid defaults; these deployments have already translated into measured economic gains (DBS projects >S$1B impact in 2025 after earlier S$370M benefits) while prompting organisational shifts - DBS plans to reduce ~4,000 temporary/contract roles as AI scales and create ~1,000 AI‑related jobs - underscoring the “so what?”: real productivity, earlier risk intervention and sizable workforce change that demand governance and reskilling.

For fuller case detail see the Harvard Business School case study on DBS' AI transformation and the Singapore Economic Development Board write-up on DBS' AI journey.

Metric	Value / Source
AI models deployed	~800 (HBS / DBS)
AI use cases	~350 (HBS / DBS)
Measured economic impact	Projected > S$1B in 2025; S$370M in 2023 (DBS / EDB)
CSO Assistant outcomes	~100% transcription/solutioning accuracy; up to 20% reduced call time (EDB)
Hyper‑personalised nudges	8.6M customers engaged regionally; 3M in Singapore (EDB)
SME early‑warning success	Identified >95% of non‑performing loans 3 months early; >80% avoided default (EDB)
Workforce impact	~4,000 temp/contract roles reduction; ~1,000 new AI jobs (BBC / FintechMag)

“We today deploy over 800 AI models across 350 use cases, and expect the measured economic impact of these to exceed SGD 1 billion in 2025... the infrastructure and governance framework we established during our AI journey have put us in good stead to unlock the potential of Generative AI while managing its emergent risks.” - Piyush Gupta, DBS (HBS / DBS press)

Talent is the hinge on which Singapore's finance‑AI ambitions swing: an informal MAS survey found 44% of local financial institutions flagged an AIDA skills shortage, so MAS's industry‑led AIDA Talent Development Programme and the AIDA Centre of Excellence are built to close that gap with pragmatic, role‑focused pathways.

The AIDA Talent Consortium stitches together banks, IHLs and training providers to co‑curate curricula and match FIs to tailored programmes, while the IMDA‑backed AIDA CoE (run with partners including UOB and NUS) offers an industry‑certified qualification combining classroom learning, mentorship and on‑the‑job deployment - a route expressly designed for finance roles from data engineer to AI/ML engineer.

Corporate‑led tracks already run as multi‑stage apprenticeships (UOB's two‑year programme, for example, blends centralized training with real business use cases and plans to intake cohorts of ~100 participants), and national targets (the CoE contributes to the NAIS 2.0 aim of growing thousands of practitioners) mean certification here is tightly tied to hireable skills, not just badges.

For finance professionals picking a certification, prioritise industry‑aligned programmes that include hands‑on projects, employer mentorship and clear role tracks - that combination turns classroom theory into immediate value on the trading floor, the credit desk or the compliance line.

Programme	What it offers / Key detail
MAS AIDA Talent Development Programme - Singapore AI & Data Analytics Talent Initiative	Aggregates FI talent demand; consortium of FIs, IHLs and trainers to co‑curate finance‑specific AIDA training
AIDA Centre of Excellence (IMDA / UOB / NUS) - Industry-certified AI training in Singapore	Industry‑certified qualification with mentorship, on‑the‑job training and structured tracks; employer placements (UOB intake ~100)
UOB AIDA Centre Of Excellence Programme - Two‑year AI apprenticeship and applied projects	Two‑year programme: year‑1 centralized training, year‑2 applied projects across Data Scientist / Data Engineer / AI‑ML Engineer tracks

"Such Government – Industry partnerships are essential to ensure that the right interventions are introduced that would address current AI talent constraints."

Getting started in Singapore means pairing practical pilots with the clear governance playbook the city‑state now offers: map every model into an inventory, run structured AI impact assessments and human‑in‑the‑loop checks, and use national testing resources (notably IMDA's AI Verify toolkit and Project Moonshot) to validate generative and predictive systems before customer rollout; regulators and advisors also point firms toward the PDPC's new PET Guide, Global AI Assurance Sandbox and national data standard to reduce legal and privacy surprises (see PDPC/IMDA summaries).

Begin with a high‑ROI use case - document processing, AML triage or a customer‑service copilot are common starters - then harden vendor contracts, logging and red‑teaming so compliance is repeatable, not ad‑hoc.

Talent matters as much as tech: non‑technical teams can gain workplace‑focused skills via programs like Nucamp's Nucamp AI Essentials for Work bootcamp (practical promptcraft and job‑based AI skills), while leadership ties pilots to MAS/IMDA frameworks to scale responsibly.

Treat the first deployment like a live regulated product - inventory, owner, tests, and a rollback plan - and the result will be measured value instead of regulatory headaches.

Bootcamp	Key details
AI Essentials for Work	15 weeks; practical AI skills, prompt writing, job‑based AI use; early bird SGD 3,582; syllabus: Nucamp AI Essentials for Work syllabus; register: Register for Nucamp AI Essentials for Work bootcamp